GeoDist: Constrained distance calculation and associated geotools

Work in progress

This R-package allows the calculation of distances that are contrained by frontiers, islands, mountains,… Distances are calculated from a raster grid of the area.
From A to B

   A     XXX
   |   XXXXXX
   | XXXXXXXX
   |XXXXXXXX
   \ XXXXXX
    \_XX_ _ B

These distances can then be implemented in classical geotools like inverse distance interpolation (idw.dist) or kriging with a modified version of geoR functions that accept custom calculated distances (variog.dist, likfit.dist, krige.conv.dist).

Currently distances are calculated using library igraph, but library Rvcg would be much more efficient (I keep that in mind for one day…).

Download and Install

To download the development version of the GeoDist package, type the following at the R command line:

install.packages("devtools")  
devtools::install_github("statnmap/GeoDist")

Note that spatial libraries like rgdal and sp may require additional softwares to be installed on your computer if you work with Mac or Linux. Look how to install proj4, geos and gdal on your system.

Example

library(GeoDist)
#> Loading required package: geoR
#> --------------------------------------------------------------
#>  Analysis of Geostatistical Data
#>  For an Introduction to geoR go to http://www.leg.ufpr.br/geoR
#>  geoR version 1.7-5.2.1 (built on 2016-05-02) is now loaded
#> --------------------------------------------------------------
library(raster)
#> Loading required package: sp
library(sp)

## basic example code
crs_merc <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m"
# Create raster
r <- raster(ncol = 20, nrow = 20,
            crs = crs_merc)
values(r) <- 1
values(r)[125:127] <- NA
values(r)[145:147] <- NA
values(r)[165:167] <- NA
plot(r)

# Create points
from_coords <- cbind(-150, 40)
from_sp <- SpatialPointsDataFrame(coords = from_coords, 
                                  data = data.frame(label = "start"),
                                  proj4string = CRS(crs_merc))

to_coords <- matrix(c(50, 0, 100, -50), byrow = TRUE, ncol = 2)
to_sp <- SpatialPointsDataFrame(coords = to_coords, 
                                data = data.frame(label = c("end1", "end2")),
                                proj4string = CRS(crs_merc))


plot(r)
points(from_sp, pch = 20, col = "blue")
points(to_sp, pch = 20, col = "red")

# For "from" and "to" being SpatialPoints
allpath <- dist.obstacle(from = from_sp, to = to_sp, r.ref = r, keep.path = TRUE)
#> step.ang was set to 5 degrees. This conducts to potential error of 0.095178 % of distances calculated.
#> r.ref has been stored in temporary directory: /tmp/RtmpFcgDIi/r.ref.grd

# All path from "from[1,]" to all "to"
lines(allpath$listLines)

Please note that the ‘GeoDist’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

statnmap/GeoDist

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